A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics
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Potriquet, J
Shah, AK
Reed, S
Jayakody, B
Kapil, C
Midha, MK
Moritz, RL
Lepletier, A
Mulvenna, J
Miles, JJ
Hill, MM
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Data independent analysis (DIA) exemplified by sequential window acquisition of all theoretical mass spectra (SWATH-MS) provides robust quantitative proteomics data, but the lack of a public primary human T-cell spectral library is a current resource gap. Here, we report the generation of a high-quality spectral library containing data for 4,833 distinct proteins from human T-cells across genetically unrelated donors, covering ~24% proteins of the UniProt/SwissProt reviewed human proteome. SWATH-MS analysis of 18 primary T-cell samples using the new human T-cell spectral library reliably identified and quantified 2,850 proteins at 1% false discovery rate (FDR). In comparison, the larger Pan-human spectral library identified and quantified 2,794 T-cell proteins in the same dataset. As the libraries identified an overlapping set of proteins, combining the two libraries resulted in quantification of 4,078 human T-cell proteins. Collectively, this large data archive will be a useful public resource for human T-cell proteomic studies. The human T-cell library is available at SWATHAtlas and the data are available via ProteomeXchange (PXD019446 and PXD019542) and PeptideAtlas (PASS01587).
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Scientific Data
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7
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1
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© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Clinical sciences
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Weerakoon, H; Potriquet, J; Shah, AK; Reed, S; Jayakody, B; Kapil, C; Midha, MK; Moritz, RL; Lepletier, A; Mulvenna, J; Miles, JJ; Hill, MM, A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics, Scientific Data, 2020, 7 (1), pp. 412